Beginner’s guide to Web Scraping in Python using BeautifulSoup

Sunil Ray 21 May, 2024
10 min read


  • Learn web scraping in Python using the BeautifulSoup library
  • Web Scraping is a useful technique to convert unstructured data on the web to structured data
  • BeautifulSoup is an efficient library available in Python to perform web scraping other than urllib
  • A basic knowledge of HTML and HTML tags is necessary to do web scraping in Python


The need and importance of extracting data from the web is becoming increasingly loud and clear. Every few weeks, I find myself in a situation where we need to extract data from the web to build a machine learning model.

For example, last week we were thinking of creating an index of hotness and sentiment about various data science courses available on the internet. This would not only require finding new courses, but also scraping the web for their reviews and then summarizing them in a few metrics!

This is one of the problems / products whose efficacy depends more on web scraping and information extraction (data collection) than the techniques used to summarize the data.In this article, you will get to learn and how to implement the Web Scarping in Python Using BeautifulSoup.

Note: We have also created a free course for this article – Introduction to Web Scraping using Python. This structured format will help you learn better.

Ways to extract information from web

There are several ways to extract information from the web. Use of APIs being probably the best way to extract data from a website. Almost all large websites like Twitter, Facebook, Google, Twitter, StackOverflow provide APIs to access their data in a more structured manner. If you can get what you need through an API, it is almost always preferred approach over web scraping. This is because if you are getting access to structured data from the provider, why would you want to create an engine to extract the same information.

Sadly, not all websites provide an API. Some do it because they do not want the readers to extract huge information in a structured way, while others don’t provide APIs due to lack of technical knowledge. What do you do in these cases? Well, we need to scrape the website to fetch the information.

There might be a few other ways like RSS feeds, but they are limited in their use and hence I am not including them in the discussion here.

web scraping, beautifulsoup, python

What is Web Scraping?

Web scraping is a computer software technique of extracting information from websites. This technique mostly focuses on the transformation of unstructured data (HTML format) on the web into structured data (database or spreadsheet).

You can perform web scraping in various ways, including use of Google Docs to almost every programming language. I would resort to Python because of its ease and rich ecosystem. It has a library known as ‘BeautifulSoup’ which assists this task. In this article, I’ll show you the easiest way to learn web scraping using python programming.

For those of you, who need a non-programming way to extract information out of web pages, you can also look at . It provides a GUI driven interface to perform all basic web scraping operations. The hackers can continue to read this article!

Steps Involved in Web Scraping

BeautifulSoup is a popular Python library used for parsing HTML and XML documents. It’s particularly useful for web scraping tasks. Here’s a breakdown of its key functionalities:

  • Parsing HTML and XML: BeautifulSoup can process HTML and XML code, transforming it into a tree-like structure that’s easier to navigate and manipulate.
  • Extracting Data: Once the document is parsed, BeautifulSoup allows you to efficiently extract specific data from it. You can target elements based on HTML tags, attributes, text content, or a combination of these criteria.
  • Web Scraping: By parsing HTML content from websites, BeautifulSoup becomes a powerful tool for web scraping. You can automate the process of collecting data from websites, which can be useful for various purposes like price comparison, data analysis, or research.
  • Handling Malformed Markup: Beautiful Soup is quite tolerant of poorly formatted HTML code, which is often the case with real-world websites. It can still parse the content effectively even if the HTML structure isn’t perfect.

Web scraping involves extracting data from websites in an automated way. Here’s the typical steps involved:

  1. Identify your target: First, you need to know what website you want to scrape data from. Be sure the website allows scraping, as some have terms of service that prohibit it.
  2. Inspect the website structure: Websites are built using HTML code. You’ll want to understand how the data you want is organized on the webpage. Most browsers have built-in developer tools that can help you view this code.
  3. Extract the data: Once you understand the website’s structure, you can use various techniques to target and extract the specific data you need. This often involves tools or writing scripts that can parse the HTML code and pinpoint the data elements.
  4. Store the data: The extracted data needs to be saved in a structured format for later use. Common formats include CSV (comma-separated values) or JSON (JavaScript Object Notation), which can be easily imported into spreadsheets or databases.

What is BeautifulSoup used for?

Libraries required for web scraping

As we know, Python is an open source programming language. You may find many libraries to perform one function. Hence, it is necessary to find the best to use library. I prefer BeautifulSoup (Python library), since it is easy and intuitive to work on. Precisely, I’ll use two Python modules for scraping data:

  • Urllib2: It is a Python module which can be used for fetching URLs. It defines functions and classes to help with URL actions (basic and digest authentication, redirections, cookies, etc). For more detail refer to the documentation page. Note: urllib2 is the name of the library included in Python 2. You can use the urllib.request library included with Python 3, instead. The urllib.request library works the same way urllib.request works in Python 2. Because it is already included you don’t need to install it.
  • BeautifulSoup: It is an incredible tool for pulling out information from a webpage. You can use it to extract tables, lists, paragraph and you can also put filters to extract information from web pages. In this article, we will use latest version BeautifulSoup 4. You can look at the installation instruction in its documentation page.

BeautifulSoup does not fetch the web page for us. That’s why, I use urllib2 in combination with the BeautifulSoup library.

Python has several other options for HTML scraping in addition to BeatifulSoup. Here are some others:

Basics – Get familiar with HTML (Tags)

html, html tags

While performing web scarping, we deal with html tags. Thus, we must have good understanding of them. If you already know basics of HTML, you can skip this section. Below is the basic syntax of HTML:This syntax has various tags as elaborated below:

  1. <!DOCTYPE html> : HTML documents must start with a type declaration
  2. HTML document is contained between <html> and </html>
  3. The visible part of the HTML document is between <body> and </body>
  4. HTML headings are defined with the <h1> to <h6> tags
  5. HTML paragraphs are defined with the <p> tag

Other useful HTML tags are:

  1. HTML links are defined with the <a> tag, “<a href=“”>This is a link for</a>”
  2. HTML tables are defined with<Table>, row as <tr> and rows are divided into data as <td>
    html table
  3. HTML list starts with <ul> (unordered) and <ol> (ordered). Each item of list starts with <li>

If you are new to this HTML tags, I would also recommend you to refer HTML tutorial from W3schools. This will give you a clear understanding about HTML tags.

Scraping a web page using BeautifulSoup

Here, I am scraping data from a Wikipedia page. Our final goal is to extract list of state, union territory capitals in India. And some basic detail like establishment, former capital and others form this wikipedia page. Let’s learn with doing this project step wise step:

  1. Import necessary libraries:
#import the library used to query a website
import urllib2 #if you are using python3+ version, import urllib.request
#specify the url
wiki = ""
#Query the website and return the html to the variable 'page'
page = urllib2.urlopen(wiki) #For python 3 use urllib.request.urlopen(wiki)
#import the Beautiful soup functions to parse the data returned from the website
from bs4 import BeautifulSoup
#Parse the html in the 'page' variable, and store it in Beautiful Soup format
soup = BeautifulSoup(page)
  1. Use function “prettify” to look at nested structure of HTML page
    beautifulsoup, prettifyAbove, you can see that structure of the HTML tags. This will help you to know about different available tags and how can you play with these to extract information.
  1. Work with HTML tags
  1. soup.<tag>: Return content between opening and closing tag including tag.
    Out[30]:<title>List of state and union territory capitals in India - Wikipedia, the free encyclopedia</title>
  2. soup.<tag>.string: Return string within given tag
    In [38]:soup.title.string
    Out[38]:u'List of state and union territory capitals in India - Wikipedia, the free encyclopedia'
  3. Find all the links within page’s <a> tags::  We know that, we can tag a link using tag “<a>”. So, we should go with option soup.a and it should return the links available in the web page. Let’s do it.
    In [40]:soup.a 
    Out[40]:<a id="top"></a>
    beautifulsoup, find_all

    Above, you can see that, we have only one output. Now to extract all the links within <a>, we will use “find_all().

    Above, it is showing all links including titles, links and other information.  Now to show only links, we need to iterate over each a tag and then return the link using attribute “href” with get.

beautifulsoup, find_all
  1. Find the right table: As we are seeking a table to extract information about state capitals, we should identify the right table first. Let’s write the command to extract information within all table tags.

    Now to identify the right table, we will use attribute “class” of table and use it to filter the right table. In chrome, you can check the class name by right click on the required table of web page –> Inspect element –> Copy the class name OR go through the output of above command find the class name of right table.

    right_table=soup.find(‘table’, class_=’wikitable sortable plainrowheaders’) right_table beautifulsoup, find_all, tableAbove, we are able to identify right table.
  2. Extract the information to DataFrame: Here, we need to iterate through each row (tr) and then assign each element of tr (td) to a variable and append it to a list. Let’s first look at the HTML structure of the table (I am not going to extract information for table heading <th>)
    table, dataframeAbove, you can notice that second element of <tr> is within tag <th> not <td> so we need to take care for this. Now to access value of each element, we will use “find(text=True)” option with each element.  Let’s look at the code:
#Generate lists
for row in right_table.findAll("tr"):
    cells = row.findAll('td')
    states=row.findAll('th') #To store second column data
    if len(cells)==6: #Only extract table body not heading
#import pandas to convert list to data frame
import pandas as pd
python, dataframe

Finally, we have data in dataframe:

Similarly, you can perform various other types of web scraping using “BeautifulSoup“. This will reduce your manual efforts to collect data from web pages. You can also look at the other attributes like .parent, .contents, .descendants and .next_sibling, .prev_sibling and various attributes to navigate using tag name. These will help you to scrap the web pages effectively.-

But, why can’t I just use Regular Expressions?

Now, if you know regular expressions, you might be thinking that you can write code using regular expression which can do the same thing for you. I definitely had this question. In my experience with BeautifulSoup and Regular expressions to do same thing I found out:

  • Code written in BeautifulSoup is usually more robust than the one written using regular expressions. Codes written with regular expressions need to be altered with any changes in pages. Even BeautifulSoup needs that in some cases, it is just that BeautifulSoup is relatively better.
  • Regular expressions are much faster than BeautifulSoup, usually by a factor of 100 in giving the same outcome.

So, it boils down to speed vs. robustness of the code and there is no universal winner here. If the information you are looking for can be extracted with simple regex statements, you should go ahead and use them. For almost any complex work, I usually recommend BeautifulSoup more than regex.


In this article, we looked at web scraping methods using “BeautifulSoup” and “urllib2” in Python. We also looked at the basics of HTML and perform the web scraping step by step while solving a challenge. I’d recommend you to practice this and use it for collecting data from web pages.

Did you find this article helpful? Please share your opinions / thoughts in the comments section below.

Note: We have also created a free course for this article – Introduction to Web Scraping using Python. This structured format will help you learn better.

If you like what you just read & want to continue your analytics learning, subscribe to our emailsfollow us on twitter or like our facebook page.

Frequently Asked Questions

Q1. How do you use BeautifulSoup for web scraping in Python?

1. Install BeautifulSoup: pip install beautifulsoup4
2. Use it to parse HTML content and extract data.
3.Example code:
from bs4 import BeautifulSoup
import requests

url = ‘’
response = requests.get(url)
html_content = response.content

soup = BeautifulSoup(html_content, ‘html.parser’)
links = soup.find_all(‘a’)

for link in links:

Q2. What are BS4 and BeautifulSoup in Python?

1.”BS4″ stands for BeautifulSoup version 4, the latest version of the library.
2.BeautifulSoup is a Python library for web scraping.
3.It simplifies parsing HTML and XML to extract desired data.

Q3.Is BeautifulSoup the best for web scraping?

1.BeautifulSoup is widely used and user-friendly.
2. Whether it’s the best depends on the project needs.
3.Other options like Scrapy, lxml, and Selenium also suit specific requirements. Choose based on project complexity and preferences.

Q4. Should I use Scrapy or BeautifulSoup?

Scrapy vs BeautifulSoup:
Simple project/learning? BeautifulSoup (easy to use).
Large project/complex scraping? Scrapy (powerful framework).pen_spark

Sunil Ray 21 May, 2024

I am a Business Analytics and Intelligence professional with deep experience in the Indian Insurance industry. I have worked for various multi-national Insurance companies in last 7 years.

Frequently Asked Questions

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Responses From Readers


Mohammad 23 Oct, 2015

Hi Sunil, Thanks for such a great article. I am new in data science area but you people make me confident to be a good analyst. Carry on.

YM 03 Nov, 2015

Thank you for the article! I am taking an online course and was looking all over the web to understand Beautiful Soup. I find your article most helpful.

Mohammad 07 Nov, 2015

Hi, I tried to complete this work with Python 2.7.10, Pycharm and ipython notebook. All my efforts failed for soup =BeautifulSoup(page) ipython error : IncompleteRead python 2.7 and Pycharm error : TypeError: 'module' object is not callable What to do ? Please help

Diddy 16 Nov, 2015

thank you!

Rakesh 19 Nov, 2015

Hi Sunil, Nice explanation it helped me understand more about data scraping through python. Just a little update I don't know whether it's some version issue or something else. You 've mentioned "find_all" in the script, which when I ran has thrown an error, after exploring more on the web I found "findAll" (underscore removed and A in caps) which worked for me. Thanks

SG 23 Nov, 2015

Excellent Article. Very concise, thorough and simple to understand. I would greatly appreciate other examples of grabbing data from a website and displaying results in a dataframe ( table )... If it's not too much of an inconvenience, could you provide example similar to above for obtaining best gasoline / petroleum prices in a particular region or at least point us to some good reference material? I just have to figure out how to get pandas installed on my windows 8.1 system. I installed portable python, which is basically running python from a folder. Guess I'll have to download pandas into that folder similar to how I did BeautifulSoup4. Thanks again...

Datahut 05 May, 2016

Thanku for this informative blog post. I liked it so much.

Arun 09 Jun, 2016

Nice article , thanks for the effort

Surendra Varma
Surendra Varma 06 Aug, 2016

Very good article, easy to understand.

Utsav Maniar
Utsav Maniar 16 Aug, 2016

Thank you for the great article. Can you please make or suggest some tutorial on how to use API to extract data from websites like twitter and perform sentiment analysis?

Baseer 17 Aug, 2016

Now it's my habit to learn a one small thing from AV, Indeed thanks for great to learn in this article

Pavan Kumar
Pavan Kumar 09 Sep, 2016

That's a very precise and neat article,Learnt basic knowledge about beautiful soup

Shabbir 07 Dec, 2016

Nice Article!! Beautifulsoup vs regex anology: web content is like money in a digital vault. Beautiful soup is a clean and process driven way of opening the vault door. Whereas, regex way is like breaking that door with a hammer!! Both are good. I somehow prefer the hammer way.

abiya 08 Dec, 2016

Great list of plugins friend. The Tables still serve a purpose. Although in the era of responsive sites it is a must have to be able to display tables responsively. League Table looks great! Definitely look into this and it has come at the right time as I am working on a site to list statistics and a table like this will work perfectly.

Shanthi 13 Dec, 2016

Really nice exercise! Thank you

Jerome Dixon
Jerome Dixon 04 Jan, 2017

Very good article. I would add a note about Selenium. I like to use Selenium and Beautiful Soup together though they overlap in functionality. Selenium can click through webpage, submit passwords, and extract data but Beautiful Soup much easier to use...together they work very well for multiple use cases.

Vikash 06 Feb, 2017

Simply awesome! Great help! Thank you so much.

Christos 20 Mar, 2017

Excellent, to the point article!

Ramakant sharma
Ramakant sharma 29 Mar, 2017

simply awesome....big thanks ..keep posting

ben taylor
ben taylor 03 Apr, 2017

Excellent article Sunil. It was simple enough for someone like me who has very basic html knowledge to implement.

Zuri 11 Apr, 2017

This was a great article! As a beginner to web scraping you explained everything very well. Thanks for sharing!

udaykumarp 05 May, 2017

great one

yogesh 24 May, 2017

very nice article for beginner

Yong 05 Jun, 2017

Very nice and easy. However, doesn't it work for Python3?

Avi 07 Jun, 2017

A good one

Nikhil 08 Jun, 2017

Nice post

Fit 27 Jun, 2017

Hi, Thanks for the useful page. Sorry if this is dumb, but do we write all this in our Jupyter books ( I am using Anaconda for Piton 2.7.12 )

Margene 30 Jun, 2017

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ARUN SINGH 05 Jul, 2017

Hi Sunil , your articles has helped alot in understanding the concept better . i have some doubts that i would like to ask you , as i use writing code to scrap data from my twitter account and its basically that i want to list the number of follower i have , till 'soup.a' it is working fine but after that i m not able to identify which tag to use . please help !!

L. Ansari
L. Ansari 14 Jul, 2017

Thanks for the wonderful resource! Though I'd recommend using python's 'requests' library as opposed to urllib2 - it is more up-to-date and easier to use in my opinion! This is by far my favorite site on the web!!

Abdoul 30 Jul, 2017

It's a big great article, thnk you.

Vinod 26 Aug, 2017

Hi Sunil , I could replicate same code in my environment , it was my first successful web scrapping practice , after this example i am sure i would be able same logic at many places. Thanks for your efforts and helping us to learn it in very simple and easy way..

Shubham Agarwal
Shubham Agarwal 10 Sep, 2017

It turn out to be a great help for me Thank you : )

Vivek Mehra
Vivek Mehra 29 Oct, 2017

Excellent Article and easy to understand!!

satya 03 Nov, 2017

Very nice article, I tried as it is but i'm not getting tile tag i.e soup.title, Please help me

tamunoemi 03 Nov, 2017

have you guys tried phpleafnode? the act of web scraping is to get visible contents found on web site. These contents are mostly displayed by Leaf nodes. Hence people need not stress themselves to initially find the position of the elements to get rules. To leverage these difficulties, phpleafnode is built. PHPLeafNode will accept your html and give you the leaf nodes in array form. it can be extended to do auto pattern recognition for you from which you can omit array indexes you think the values are unneeded. and the needed array indexes gets your expected values. Phpleafnode can be downloaded from github.

sivanagamahesh 14 Dec, 2017

Hi, Thank you so much for posting this. I really appreciate your work. Keep it up. Great work!

Ágata 05 Mar, 2018

Thank you! It was really clear and helpful! Craving to learn more here =)

KUMAR G 10 Mar, 2018

how about scrapy? which one you prefer scrapy or beautiful soup? could you give me a calrity?

Anjaneya 07 Apr, 2018

Really helpful...Thanks much!!!

Krishna sharma
Krishna sharma 07 Apr, 2018

Thanks a ton guys!!!

luv jain
luv jain 21 Apr, 2018

Can anyone help me to find a particular paragraph with a heading, from multiple web pages having same heading available?

Aarti Popshetwar
Aarti Popshetwar 25 May, 2018

which text editor you have to used for this whole code execution like import, and other